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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems

作者： Xiaoqi Wang Yiming Fang Miao Xu Zhichao Lei Key Labpratory of Industrial Computer Control Engineering Yanshan University Qinhuangdao Hebei Province China Engineering Research Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao Hebei Province China

For a class of second-order nonlinear leader-following multi-agent systems with actuator faults and integral quadratic constraints (IQCs) of followers, a fully distributed adaptive consensus control algorithm based on improved PID sliding mode is designed. The algorithm only requires local topology information but no global information. According to Lyapunov stability theory, it is demonstrated that multi-agent systems can converge to the vicinity near equilibrium points within a finite time. Finally, the feasibility of the control algorithm proposed in this paper is verified by numerical simulation of multi-agent systems.

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A Heterogeneous Reinforcement Learning Approach for Joint Relay Selection and Power Allocation in Time-Varying Uasns with Energy Harvesting

SSRN

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SSRN 2024年

作者： Han, Song Wang, Ziyuan Li, Aijia Li, Xinbin Liu, Zhixin Yan, Lei Zhang, Tong Wei Key Lab of Industrial Computer Control Engineering of Hebei Province Institute of Electrical Engineering Yanshan University Hebei Province Qinhuangdao066004 China School of Computer and Communication Engineering Northeastern University Hebei Province Qinhuangdao066004 China Department of Technology Northeastern University Shandong Province Qingdao266237 China

In this study, we investigate the joint relay selection and power allocation problem for underwater acoustic communication networks with energy harvesting to maximize the cumulative capacity. We specially formulate a heterogeneous reinforcement learning-based framework. In the different decision layers, the Upper Confidence Bound 1 and enhanced Deep Deterministic Policy Gradient algorithms are employed to efficiently explore the discrete and continuous action spaces, respectively. Therefore, the solving difficulty of the complex hybrid discrete-continuous optimization problem can be significantly mitigated. Furthermore, a circular interaction structure is constructed to facilitate the exchange of decision-making information between the two decision layers. The strongly interdependent relationship of the different substrategies can be comprehensively considered, thereby improving the entire performance of thejoint strategy. Moreover, we propose an adaptive reward function, which can guide the user to balance the instantaneous capacity and interruptions, thereby effectively utilizing the green energy harvesting technology and enhancing long-term cumulative performance. Simulation results verify the effectiveness of our proposed algorithm. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Rollover Prevention control for Autonomous Vehicles with High Centers of Gravity Considering Input Saturation

Rollover Prevention Control for Autonomous Vehicles with Hig...

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第43届中国控制会议

作者： Linying Lu Ting Zhang Lin Qi Jiayi Fang Xiaohong Jiao School of Electrical Engineering Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University School of Vehicle and Mobility Tsinghua University School of Mechanical Engineering Beijing Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

A fixed-time terminal sliding mode control(FTSMC) strategy based on extended state observer for cooperative control of rollover prevention and trajectory tracking is proposed to enable the high-center-of-gravity autonomous vehicles to ensure tracking performance and prevent rollover. The FTSMC allows the errors to converge globally to a predefined arbitrarily small set centered at zero in a fixed time and then exponentially converge to zero while weakening the influence of input saturation on the controller by compensating the auxiliary system of input saturation. Simulation comparisons with existing results verify the effectiveness and advantages of the designed strategy under the MATlab/Simulink environment.

关键词： Autonomous Vehicles Fixed-time Rollover Prevention Input Saturation

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Pose Analysis of Humanoid Robot Imitation Process Based on Improved MLP Network

Pose Analysis of Humanoid Robot Imitation Process Based on I...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Shuhuan Wen Yang Liu Leibo Zheng Fuchun Sun Bin Fang Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Dept. of Computer Science and Technology Tsinghua University Beijing China

This paper proposes a system, by which humanoid robots could imitate human movements to learn human behaviors. Considering the security problem during humanoid robot imitation process, a method of stability identification based on improved Multi-Layer Perception (MLP) is proposed. Firstly, the positions of the human joints are collected by a Kinect. Secondly, the human joints information is transformed into robot angles information by the inverse kinematics equation. Finally, the stability of the robot is identified by the deep neural network, which has the robot angles as its input. Simulations and experimental results demonstrate that compared with the Back Propagation neural network (BPnn), RBF neural network (RBFnn), Support Vector Machine (SVM) and Extreme Learning Machine (ELM), MLP neural network has the best identification accuracy. And the filters we used can overcome the problem of speed pulse, to some extent, and achieved an ideal imitation result.

关键词： Neural networks Humanoid robots Robot kinematics Stability analysis Torso Shoulder

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The improved load balance adjustor of multi-motor driving system

The improved load balance adjustor of multi-motor driving sy...

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Chinese control Conference (CCC)

作者： Jing Ming Yang Bo Su Jin Lv Hai Jun Che Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 China Tian Jin Design and Research Institute of Electric Drive Tianjin 300180 China

Multi-motor coaxial connected are used to drive large mechanical load motor drive system. Multi-motor driver's main advantage is can reduce the rotational inertia of the load, increase the rapidity of system, but followed by many motor driver control difficulty increase. Based on one tandem mill motor drive as the background, analyzes the series motor drive a rack rolling work mode, aiming at the load problem of this way of driving motor, design a new kind of load balance adjustor. Using load observer to get the actual load as the load balance adjustor's input, the new control method of quadratic performance indicators single neuron adaptive PID, and joined the feed forward compensation to inhibit speed fluctuations. The results show that, this method can substantially ensure the roll motor load balance, while having a good control effect.

关键词： Observers Neurons Motor drives MATlab Design methodology Channel hot electron injection Digital control

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Nonlinear Identification of Triple Inverted Pendulum Based on GA-RBF-ARX

Nonlinear Identification of Triple Inverted Pendulum Based o...

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第35届中国控制会议

作者： Hai-bin Li Shuang Hao Xiu-ling Zhang Yong-jin Lai Qing Qi Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Yanshan University

ISBN: (纸本)9781509009107

The triple inverted pendulum is a nonlinear, unsteady and dynamic system. The traditional model is built based on mathematical modeling method which has ignored many important factors in reality. In this paper, the GA-RBF-ARX is proposed to identify a nonlinear triple inverted pendulum model based on its input/output data. RBF-ARX model is a combination of Gaussian radial basis function(RBF) neural network and Autoregressive model with exogenous input(ARX) model. It not only has the advantages of RBF neural network, such as the approximation ability, simple structure and quickly learning rate, but also has the ability of describing globally of ARX model. The structured nonlinear parameter optimization method(SNPOM) is generally used to optimize parameters of the RBF-ARX model. However, SNPOM needs to classify the parameters before optimization. It is too complex to popularize and apply in practical engineering. Due to this problem, genetic algorithm(GA) is proposed to replace SNPOM. The identification process based on GA is simpler than SNPOM. Besides, GA has good parallel design structure and characteristics of global optimization. Finally, the MATlab simulation results show that GA-RBF-ARX identification is effective.

关键词： GA-RBF-ARX nonlinear triple inverted pendulum identification optimize

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Stability analysis of periodic solutions and time-delay feedback control of the torsional vibration system of the rolling mill

Stability analysis of periodic solutions and time-delay feed...

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第六届工程机械与车辆工程新进展国际学术会议

作者： Shuang Liu Shuxian Tan Shuo Li Yaqian Li Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Yanshan University

In this paper,considering the influence of nonlinear factors and Lagrange principle,the general dynamic equation of the nonlinear torsional vibration system with nonlinear stiffness and excitation is

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Application of artificial bee colony algorithm to select architecture of a optimal neural network for the prediction of rolling force in hot strip rolling process

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Journal of Chemical and Pharmaceutical Research 2013年第9期5卷 563-570页

作者： Zhao, Zhiwei Yang, Jingming Che, Haijun Sun, Hao Yang, He Key Lab of Industrial Computer Control Engineering of Hebei Province Institution of Electrical Engineering Yanshan University Qinhuangdao China National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Yanshan University Qinhuangdao China Department of Computer Science and Technology Tangshan College Tangshan China

In the face of global competition, the requirement for dimensional accuracy, mechanical properties and surface properties has become a major challenge on aluminum manufacturing industries. Conventional rolling force formulas, however, provide not more than reasonably exact approximations. The mathematical modeling of the hot rolling process has long been recognized to be a desirable approach to aid in rolling operating practice and the design of mill equipment to improve productivity and quality. However, many factors make the theoretical analysis of the rolling process very complex and time-consuming. This paper presents a prediction method based on the Artificial Bee Colony algorithm and BP neural network, which was developed in order to improve the prediction of rolling force in hot strip rolling process. The architecture of BP neural network is optimized by Artificial Bee Colony algorithm. Comparing with the Sims mathematical model and BP neural network, the experimental results show that the prediction accuracy and error of rolling force is superior to the other two methods, and the predicted rolling force is very close to the practical rolling force.

关键词： Neural networks

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ICA and PIND based Remainder Particle Detection Technique for Space-borne Equipment

ICA and PIND based Remainder Particle Detection Technique fo...

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IEEE International Conference on Robotics and Biomimetics

作者： Zhishuai Jiang Zengshuai Qiu Yuanda Tian Junyu Shi Chengzhong Yu Hongzhi Gao Yuliang Zhao School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China Key Lab of Industrial Computer Control Engineering of Hebei Province School of Electrical Engineering Yanshan University Qinhuangdao China DFH Satellite Co. Ltd. Beijing China

ISBN: (纸本)9781665405362

Space-borne equipment is a major component of the satellite system, which plays a very important role in the process of satellite launch and reception. Therefore, the satellite system has high requirements for the reliability and safety of space-borne equipment. The remainder particle are metal and non-metal particles inside the space-borne equipment that affect the normal operation of the space-borne equipment. When the space-borne equipment is in a weightless state, the remainder particle will produce irregular movement accordingly cause huge safety hazards to the space-borne equipment. Particle impact noise detection (PIND) by way of vibrating the space-borne equipment cause remainder particle in the space-borne equipment to collide with the inner wall of the space-borne equipment. Then, the acoustic emission sensor is used to receive the sound generated by the vibration of the remainder particles. We can analyze the received sound signal to determine whether the space-borne equipment contains remainder particle. However, how to avoid the interference of environmental noise has always been a thorny issue in the process of using PIND to detect remainder particle. In the process of detecting noise, there may be environmental noise that will inevitably affect the detection of remainder particle. In order to filter out the environmental noise, we recorded mixed audio of the remainder particle. Then the ICA algorithm is used to separate the remainder particle signal. Through many experiments, we learn that the recognition rate of this judgment method reaches 100%.

关键词： Vibrations Satellites Working environment noise Metals Transforms Interference Robot sensing systems

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Tension control Study in the Finishing Mill of Aluminum Hot Rolling Mills based on PSO+CMAC

Tension Control Study in the Finishing Mill of Aluminum Hot ...

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2013 3rd International Conference on Advanced Measurement and Test(AMT 2013)

作者： YANG Jing-ming CHEN Li Wang Wan-hong CHE Hai-jun Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University National Engineering Research Center for Equipment and Technology of C.S.R Yanshan University Henan Mingtai Al.Industrial Co.Ltd of

Aimed at decreasing the tension variation during transforming process of "1+4" aluminum hot rolling mills unit in one factory of Henan,we analyzed the factors affecting tension variation and improved the tension formula of hot rolling mills process considering the influence of tension on forward slip *** tension forming process of hot rolling mills from stand 1 to stand 4 is simulated,and the tension structure diagrams of the four-stands are *** to nonlinear characteristics of the aluminum hot rolling mills tension system,conventional PID controller could not meet the requirement of the *** we proposed the mixed control of Cerebellar Model Articulation controller(CMAC)coupled with PID to solve the problem of online parameters control and we used Particle Swarm Optimization(PSO)algorithm to optimize PID controller *** simulation,it could be found that the using of PSO+CMAC significantly reduced surplus adjustment and transition time,and strip tension coupling degree among stands was also reduced.

关键词： aluminum hot rolling mills tension model tension influence coefficient PSO CMAC

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